Presentation by Prof. Gerard De Jong delivered on on 20 March 2014 at International Freight Transport Modelling Workshop 2014 http://bit.ly/1o0vxAh www.its.leeds.ac.uk/people/g.de+jong

1. Freight transport models with logistics
in data-rich and not so rich environments
Gerard de Jong - Significance, ITS Leeds
20 March 2014

2. Motto
“Data! Data! Data!”, he cried impatiently, “I can’t
make bricks without clay.”
Sherlock Holmes
The Adventure of the Copper Beeches
Sir Arthur Conan Doyle
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3. General Modelling Framework (see Ben-Akiva and de
Jong; in Ben-Akiva, Meersman and van de Voorde (2013))
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Economic activity
• Growth factor
• Gravity
• Input-output
• Spatial equilibrium
Logistics choices
• Inventory
• Transport chains
Network assignment
Production-consumption flows
Vehicular flows

4. Examples of PC transport chains
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road rail road
P C
road inland waterways road
P C

5. A logistics model
 reads in base matrices of commodity flows from producers
to consumers : PC flows
 delivers OD matrices to the network model (assignment)
 determines shipment size and transport chain
 Arguments to do this at the disaggregate level
 Examples: SMILE (Netherlands), EUNET (UK), Maurer
(UK), ADA (Sweden, Norway, Denmark, Flanders), Liedtke
(Germany), Friedrich (Germany), Combes (France), Samimi
et al. (US).
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6. Typology of data in freight transport (from Tavasszy
and de Jong, 2014, chapter 10)
International trade statistics
National accounts data
Transport statistics by mode
Shippers surveys
Project-specific interviews (incl. stated preference)
Consignment bills and RFIDs – BIG DATA?
Traffic count data – BIG DATA
Traffic safety inspection data
Network data
Cost functions
Terminal data
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7. Big data in transport
 From automatic traffic count equipment or GPS
 Often rather big (many records) but not very deep (few variables)
 Lots of info on LHS variables, not much on RHS (explanatory variables)
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8. The Swedish Commodity Flow Survey
 Carried out by Statistics Sweden for transport authorities
 A sample of Swedish production and wholesale companies was asked
to record their shipments in a 1-3 week period
 Outgoing shipments (domestic and international) and incoming
(international)
 Records=shipments; CFS 2009: 3.5 mln outgoing shipments
 Includes data on production and consumption location (municipality
level), industry, weight, value, commodity type and mode chain (e.g.
truck-train-truck)
 CFS 2001 and 2004/2005 have been used in previous analyses
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9. The French ECHO survey
 Envois-Chargeurs-Opérateurs 2004 (ECHO); IFSTTAR plans a new
ECHO
 Carried out by IFSTTAR (previously INRETS) and ISL
 Starting point: a sample of almost 3,000 French shippers: last
shipments in up to 3 last months
 Reconstituted for almost 10,000 shipments the full transport chain
(PC) by also interviewing 27,000 receivers, transport operators and
LSPs
 Data includes attributes of the firms involved, locations of
production, consumption and transhipment (NUTS3 level), annual
flow, weight, volume, commodity type and modes used in the chain
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10. Four situations for a logistics model
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PC model Individual
shipment data
Yes Yes Data-rich
Yes No Not so rich
No Yes Not so rich
No No Not so rich

11. Logistics model in a data-rich environment
 Estimate transport chain and shipment size models on a shippers survey
 Determine PC flows (SCGE, I/O model) and disaggregate to f2f flows
 Implement the estimated functions for shipment size and transport chain for
each f2f flow
□ Apply by calculating and summing probabilities over f2f flows
□ Gives OD flows by mode and commodity for uni-modal assignment
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12. Example - Multinomial logit model of discrete shipment size and transport
chain choice (Abate, Vierth and de Jong, 2014)
Model 1 (domestic, all
commodities, Windisch, 2009)
Model 2 (metal products)
Variable Relevant
Alternative
Coefficient
estimates
Relevant
Alternative
Coefficient
estimates
Cost All chains -0.0011*** All chains -0.0001***
Transport time (in
hours) times
value of goods (in
mln SEK)
Truck -1.98e-7***
Proxy to
Rail/Quay
Rail, Ferry,
Vessel
0.729***
Value Density All modes:
smallest 2
shipment sizes
0.122***
Value Density 1 Weight Cat 1-
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-5.79***
Value Density 2 Weight Cat 6-
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4.49***
Value Density 3 Weight Cat 1 0.961***
Time of Year
(Summer)
1.02***
Rail Constant -3.08***
Ferry Constant -4.51***
Vessel Constant -4.23***
Truck Fixed
Observations 2.225.150 33868
Final LL value -1.601.661 -77652.811
Rho2 (0) 0.737
Rho2 (C) 0.314 0.384

13. Logistics model in a not so data-rich
environment: PC model, but no shippers survey
 Deterministic transport chain and shipment size model that minimises total
logistics cost
 Determine PC flows (SCGE, I/O model) and disaggregate to f2f flows
 Implement the minimisation function for shipment size and transport chain
for each f2f flow
□ Apply by calculating the 0/1 solution and summing over f2f flows
□ Gives OD flows by mode and commodity for uni-modal assignment
□ Calibrate to observed aggregate OD transport chain shares
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14. Logistics model in a not so data-rich
environment: no PC model, no shippers survey
 Do a limited shippers survey (sample) to get individual shipments at PC level
(could focus on international flows/flows through ports)
 Estimate transport chain and shipment size model
 Apply this function on the sample and expand to observed aggregate OD flows
 For future years, grow to/from a country by country-specific growth factors.
□ Gives OD flows by mode and commodity for uni-modal assignment
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15. Conclusions
 A logistics model explains transport chain and shipment size choice
 In a data-rich situation this can be estimated on data at the level of individual
shipments (shippers survey/commodity flow survey)
 Without such a survey, there is the possibility of a deterministic model,
calibrated to aggregate OD data:
□ Weaker empirical foundation
□ Danger of flip-flop behaviour
 If also the PC model is missing, there is no choice really but to collect (a
limited amount of) shipment data:
□ To get the PC pattern
□ To estimate shipment size and transport chain models
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